Verba¶

What it is¶

Verba is an open-source Retrieval-Augmented Generation (RAG) application built on top of Weaviate.

What problem it solves¶

It provides a user-friendly interface for building RAG applications, handling data ingestion, chunking, and querying with LLMs out of the box.

Where it fits in the stack¶

Category: Tool / Knowledge Management / RAG

Typical use cases¶

Creating a personal knowledge base with AI search.
Question-answering over private document collections.
Testing different chunking and retrieval strategies.

Strengths¶

Easy to set up with Docker.
Built-in support for multiple data types (PDF, txt, etc.).
Native integration with Weaviate's vector search capabilities.

Limitations¶

Closely tied to the Weaviate ecosystem.
May require configuration for optimal performance with specific datasets.

When to use it¶

When you want a production-ready RAG interface without building it from scratch.

When not to use it¶

If you need a highly customized retrieval pipeline that departs significantly from Verba's architecture.

Licensing and cost¶

Open Source: Yes (BSD-3-Clause)
Cost: Free
Self-hostable: Yes

Getting started¶

Docker Deployment¶

The most reliable way to run Verba is via Docker Compose, which packages the frontend, backend, and Weaviate database.

git clone https://github.com/weaviate/Verba
cd Verba
# Set your API keys in the .env file
docker compose up -d

PIP Installation¶

pip install goldenverba
verba start

API examples¶

Verba exposes a backend API that can be used to programmatically ingest data or query the RAG pipeline.

Query via Python¶

import requests

url = "http://localhost:8000/api/query"
payload = {
    "query": "How do I configure the OIDC middleware for Traefik?",
    "conversation_id": "optional-id"
}

response = requests.post(url, json=payload)
print(response.json()["answer"])

Weaviate — The vector database powering Verba.
Khoj — Alternative RAG assistant for personal notes.
AnyType — Local-first P2P knowledge base.
RAG Pattern — Underlying architectural concept.
Obsidian — Can be used as a data source via Markdown export.
LangChain — Often used in conjunction with Weaviate for custom pipelines.
Ollama — Supported as a local inference backend.

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-05-13
Confidence: high